https://doi.org/10.5281/zenodo.3635430
Histopathology Research Template 🔬
Describe Materials and Methods as highlighted in (Knijn, Simmer, and Nagtegaal 2015).2
Describe patient characteristics, and inclusion and exclusion criteria
Describe treatment details
Describe the type of material used
Specify how expression of the biomarker was assessed
Describe the number of independent (blinded) scorers and how they scored
State the method of case selection, study design, origin of the cases, and time frame
Describe the end of the follow-up period and median follow-up time
Define all clinical endpoints examined
Specify all applied statistical methods
Describe how interactions with other clinical/pathological factors were analyzed
Codes for general settings.3
Setup global chunk settings4
knitr::opts_chunk$set(
eval = TRUE,
echo = TRUE,
fig.path = here::here("figs/"),
message = FALSE,
warning = FALSE,
error = FALSE,
cache = FALSE,
comment = NA,
tidy = TRUE,
fig.width = 6,
fig.height = 4
)Load Library
see R/loadLibrary.R for the libraries loaded.
Codes for generating fake data.5
Generate Fake Data
This code generates a fake histopathological data. Some sources for fake data generation here6 , here7 , here8 , here9 , here10 , here11 , here12 , here13 , and here14 .
Use this code to generate fake clinicopathologic data
Codes for importing data.15
Read the data
library(readxl)
mydata <- readxl::read_excel(here::here("data", "mydata.xlsx"))
# View(mydata) # Use to view data after importingAdd code for import multiple data purrr reduce
Codes for reporting general features.16
Dataframe Report
The data contains 250 observations of the following variables:
- ID: 250 entries: 001, n = 1; 002, n = 1; 003, n = 1 and 247 others
- Name: 249 entries: Aahaan, n = 1; Abdihamid, n = 1; Abdulkarim, n = 1 and 246 others (1 missing)
- Sex: 2 entries: Female, n = 139; Male, n = 110 (1 missing)
- Age: Mean = 50.09, SD = 14.54, range = [25, 73], 1 missing
- Race: 6 entries: White, n = 165; Hispanic, n = 37; Black, n = 33 and 3 others (1 missing)
- PreinvasiveComponent: 2 entries: Absent, n = 193; Present, n = 56 (1 missing)
- LVI: 2 entries: Absent, n = 161; Present, n = 89
- PNI: 2 entries: Absent, n = 169; Present, n = 80 (1 missing)
- Death: 2 levels: FALSE (n = 64); TRUE (n = 185) and missing (n = 1)
- Group: 2 entries: Treatment, n = 137; Control, n = 112 (1 missing)
- Grade: 3 entries: 3, n = 112; 2, n = 71; 1, n = 66 (1 missing)
- TStage: 4 entries: 4, n = 108; 3, n = 85; 2, n = 37 and 1 other
- AntiX_intensity: Mean = 2.38, SD = 0.66, range = [1, 3], 1 missing
- AntiY_intensity: Mean = 2.02, SD = 0.76, range = [1, 3], 1 missing
- LymphNodeMetastasis: 2 entries: Absent, n = 158; Present, n = 91 (1 missing)
- Valid: 2 levels: FALSE (n = 119); TRUE (n = 130) and missing (n = 1)
- Smoker: 2 levels: FALSE (n = 115); TRUE (n = 134) and missing (n = 1)
- Grade_Level: 3 entries: high, n = 110; moderate, n = 77; low, n = 62 (1 missing)
- DeathTime: 2 entries: Within1Year, n = 148; MoreThan1Year, n = 102
250 observations with 21 variables
17 variables containing missings (NA)
0 variables with no variance
Codes for defining variable types.19
print column names as vector
c("ID", "Name", "Sex", "Age", "Race", "PreinvasiveComponent",
"LVI", "PNI", "LastFollowUpDate", "Death", "Group", "Grade",
"TStage", "AntiX_intensity", "AntiY_intensity", "LymphNodeMetastasis",
"Valid", "Smoker", "Grade_Level", "SurgeryDate", "DeathTime")
See the code as function in R/find_key.R.
keycolumns <- mydata %>% sapply(., FUN = dataMaid::isKey) %>% as_tibble() %>% select(which(.[1,
] == TRUE)) %>% names()
keycolumns[1] "ID" "Name"
Get variable types
# A tibble: 4 x 4
type cnt pcnt col_name
<chr> <int> <dbl> <list>
1 character 11 57.9 <chr [11]>
2 logical 3 15.8 <chr [3]>
3 numeric 3 15.8 <chr [3]>
4 POSIXct POSIXt 2 10.5 <chr [2]>
mydata %>% select(-keycolumns, -contains("Date")) %>% describer::describe() %>% knitr::kable(format = "markdown")| .column_name | .column_class | .column_type | .count_elements | .mean_value | .sd_value | .q0_value | .q25_value | .q50_value | .q75_value | .q100_value |
|---|---|---|---|---|---|---|---|---|---|---|
| Sex | character | character | 250 | NA | NA | Female | NA | NA | NA | Male |
| Age | numeric | double | 250 | 50.092369 | 14.5439927 | 25 | 38 | 51 | 63 | 73 |
| Race | character | character | 250 | NA | NA | Asian | NA | NA | NA | White |
| PreinvasiveComponent | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
| LVI | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
| PNI | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
| Death | logical | logical | 250 | NA | NA | FALSE | NA | NA | NA | TRUE |
| Group | character | character | 250 | NA | NA | Control | NA | NA | NA | Treatment |
| Grade | character | character | 250 | NA | NA | 1 | NA | NA | NA | 3 |
| TStage | character | character | 250 | NA | NA | 1 | NA | NA | NA | 4 |
| AntiX_intensity | numeric | double | 250 | 2.381526 | 0.6622147 | 1 | 2 | 2 | 3 | 3 |
| AntiY_intensity | numeric | double | 250 | 2.024096 | 0.7616181 | 1 | 1 | 2 | 3 | 3 |
| LymphNodeMetastasis | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
| Valid | logical | logical | 250 | NA | NA | FALSE | NA | NA | NA | TRUE |
| Smoker | logical | logical | 250 | NA | NA | FALSE | NA | NA | NA | TRUE |
| Grade_Level | character | character | 250 | NA | NA | high | NA | NA | NA | moderate |
| DeathTime | character | character | 250 | NA | NA | MoreThan1Year | NA | NA | NA | Within1Year |
Plot variable types
# https://github.com/ropensci/visdat
# http://visdat.njtierney.com/articles/using_visdat.html
# https://cran.r-project.org/web/packages/visdat/index.html
# http://visdat.njtierney.com/
# visdat::vis_guess(mydata)
visdat::vis_dat(mydata)character variablescharacterVariables <- mydata %>% select(-keycolumns) %>% inspectdf::inspect_types() %>%
dplyr::filter(type == "character") %>% dplyr::select(col_name) %>% pull() %>%
unlist()
characterVariables [1] "Sex" "Race" "PreinvasiveComponent"
[4] "LVI" "PNI" "Group"
[7] "Grade" "TStage" "LymphNodeMetastasis"
[10] "Grade_Level" "DeathTime"
categorical variablescategoricalVariables <- mydata %>% dplyr::select(-keycolumns, -contains("Date")) %>%
describer::describe() %>% janitor::clean_names() %>% dplyr::filter(column_type ==
"factor") %>% dplyr::select(column_name) %>% dplyr::pull()
categoricalVariablescharacter(0)
continious variablescontiniousVariables <- mydata %>% dplyr::select(-keycolumns, -contains("Date")) %>%
describer::describe() %>% janitor::clean_names() %>% dplyr::filter(column_type ==
"numeric" | column_type == "double") %>% dplyr::select(column_name) %>% dplyr::pull()
continiousVariables[1] "Age" "AntiX_intensity" "AntiY_intensity"
numeric variablesnumericVariables <- mydata %>% select(-keycolumns) %>% inspectdf::inspect_types() %>%
dplyr::filter(type == "numeric") %>% dplyr::select(col_name) %>% pull() %>% unlist()
numericVariables[1] "Age" "AntiX_intensity" "AntiY_intensity"
integer variablesintegerVariables <- mydata %>% select(-keycolumns) %>% inspectdf::inspect_types() %>%
dplyr::filter(type == "integer") %>% dplyr::select(col_name) %>% pull() %>% unlist()
integerVariablesNULL
Codes for overviewing the data.20
reactable::reactable(data = mydata, sortable = TRUE, resizable = TRUE, filterable = TRUE,
searchable = TRUE, pagination = TRUE, paginationType = "numbers", showPageSizeOptions = TRUE,
highlight = TRUE, striped = TRUE, outlined = TRUE, compact = TRUE, wrap = FALSE,
showSortIcon = TRUE, showSortable = TRUE)Summary of Data via summarytools 📦
if (!dir.exists(here::here("out"))) {
dir.create(here::here("out"))
}
summarytools::view(x = summarytools::dfSummary(mydata %>% select(-keycolumns)), file = here::here("out",
"mydata_summary.html"))Summary via dataMaid 📦
if (!dir.exists(here::here("out"))) {
dir.create(here::here("out"))
}
dataMaid::makeDataReport(data = mydata, file = here::here("out", "dataMaid_mydata.Rmd"),
replace = TRUE, openResult = FALSE, render = FALSE, quiet = TRUE)Summary via explore 📦
if (!dir.exists(here::here("out"))) {
dir.create(here::here("out"))
}
mydata %>% select(-dateVariables) %>% explore::report(output_file = "mydata_report.html",
output_dir = here::here("out"))Glimpse of Data
Observations: 250
Variables: 17
$ Sex <chr> "Male", "Male", "Female", "Male", "Female", "Mal…
$ Age <dbl> 26, 31, 44, 70, 38, 67, 65, 54, 49, 41, 26, 53, …
$ Race <chr> "White", "White", "White", "White", "White", "Wh…
$ PreinvasiveComponent <chr> "Absent", "Absent", "Absent", "Present", "Absent…
$ LVI <chr> "Absent", "Present", "Absent", "Present", "Absen…
$ PNI <chr> "Absent", "Absent", "Absent", "Absent", "Present…
$ Death <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE,…
$ Group <chr> "Treatment", "Control", "Treatment", "Treatment"…
$ Grade <chr> "3", "3", "2", "3", "2", "1", "3", "3", "2", "3"…
$ TStage <chr> "1", "4", "3", "4", "3", "4", "4", "4", "4", "3"…
$ AntiX_intensity <dbl> 3, 2, 2, 3, 2, 2, 2, 2, 3, 3, 3, 1, 1, 1, 2, 2, …
$ AntiY_intensity <dbl> 3, 2, 3, 3, 1, 1, 2, 2, 2, 3, 3, 2, 2, 3, 3, 2, …
$ LymphNodeMetastasis <chr> "Absent", "Absent", "Absent", "Absent", "Absent"…
$ Valid <lgl> TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FA…
$ Smoker <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, T…
$ Grade_Level <chr> "low", "moderate", "high", "low", "high", "moder…
$ DeathTime <chr> "Within1Year", "Within1Year", "Within1Year", "Wi…
# A tibble: 21 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 ID chr 0 0 250 NA NA NA
2 Name chr 1 0.4 250 NA NA NA
3 Sex chr 1 0.4 3 NA NA NA
4 Age dbl 1 0.4 50 25 50.1 73
5 Race chr 1 0.4 7 NA NA NA
6 PreinvasiveComponent chr 1 0.4 3 NA NA NA
7 LVI chr 0 0 2 NA NA NA
8 PNI chr 1 0.4 3 NA NA NA
9 LastFollowUpDate dat 1 0.4 13 NA NA NA
10 Death lgl 1 0.4 3 0 0.74 1
# … with 11 more rows
Explore
Control Data if matching expectations
visdat::vis_expect(data = mydata, expectation = ~.x == -1, show_perc = TRUE)
visdat::vis_expect(mydata, ~.x >= 25)See missing values
$variables
Variable q qNA pNA qZero pZero qBlank pBlank qInf pInf
1 Valid 250 1 0.4% 119 47.6% 0 - 0 -
2 Smoker 250 1 0.4% 115 46% 0 - 0 -
3 Death 250 1 0.4% 64 25.6% 0 - 0 -
4 Sex 250 1 0.4% 0 - 0 - 0 -
5 PreinvasiveComponent 250 1 0.4% 0 - 0 - 0 -
6 PNI 250 1 0.4% 0 - 0 - 0 -
7 Group 250 1 0.4% 0 - 0 - 0 -
8 LymphNodeMetastasis 250 1 0.4% 0 - 0 - 0 -
9 Grade 250 1 0.4% 0 - 0 - 0 -
10 AntiX_intensity 250 1 0.4% 0 - 0 - 0 -
11 AntiY_intensity 250 1 0.4% 0 - 0 - 0 -
12 Grade_Level 250 1 0.4% 0 - 0 - 0 -
13 Race 250 1 0.4% 0 - 0 - 0 -
14 LastFollowUpDate 250 1 0.4% 0 - 0 - 0 -
15 Age 250 1 0.4% 0 - 0 - 0 -
16 SurgeryDate 250 1 0.4% 0 - 0 - 0 -
17 Name 250 1 0.4% 0 - 0 - 0 -
18 LVI 250 0 - 0 - 0 - 0 -
19 DeathTime 250 0 - 0 - 0 - 0 -
20 TStage 250 0 - 0 - 0 - 0 -
21 ID 250 0 - 0 - 0 - 0 -
qDistinct type anomalous_percent
1 3 Logical 48%
2 3 Logical 46.4%
3 3 Logical 26%
4 3 Character 0.4%
5 3 Character 0.4%
6 3 Character 0.4%
7 3 Character 0.4%
8 3 Character 0.4%
9 4 Character 0.4%
10 4 Numeric 0.4%
11 4 Numeric 0.4%
12 4 Character 0.4%
13 7 Character 0.4%
14 13 Timestamp 0.4%
15 50 Numeric 0.4%
16 219 Timestamp 0.4%
17 250 Character 0.4%
18 2 Character -
19 2 Character -
20 4 Character -
21 250 Character -
$problem_variables
[1] Variable q qNA pNA
[5] qZero pZero qBlank pBlank
[9] qInf pInf qDistinct type
[13] anomalous_percent problems
<0 rows> (or 0-length row.names)
================================================================================
[1] "Ignoring variable LastFollowUpDate: Unsupported type for visualization."
[1] "Ignoring variable SurgeryDate: Unsupported type for visualization."
Variable p_1 p_10 p_25 p_50 p_75 p_90 p_99
1 AntiX_intensity 1 1.8 2 2 3 3 3
2 AntiY_intensity 1 1 1 2 3 3 3
3 Age 25 30 38 51 63 70 73
Summary of Data via DataExplorer 📦
# A tibble: 1 x 9
rows columns discrete_columns continuous_colu… all_missing_col…
<int> <int> <int> <int> <int>
1 250 21 18 3 0
# … with 4 more variables: total_missing_values <int>, complete_rows <int>,
# total_observations <int>, memory_usage <dbl>
Drop columns
Write results as described in (Knijn, Simmer, and Nagtegaal 2015)22
Describe the number of patients included in the analysis and reason for dropout
Report patient/disease characteristics (including the biomarker of interest) with the number of missing values
Describe the interaction of the biomarker of interest with established prognostic variables
Include at least 90 % of initial cases included in univariate and multivariate analyses
Report the estimated effect (relative risk/odds ratio, confidence interval, and p value) in univariate analysis
Report the estimated effect (hazard rate/odds ratio, confidence interval, and p value) in multivariate analysis
Report the estimated effects (hazard ratio/odds ratio, confidence interval, and p value) of other prognostic factors included in multivariate analysis
Codes for Descriptive Statistics.23
Report Data properties via report 📦
The data contains 250 observations of the following variables:
- ID: 250 entries: 001, n = 1; 002, n = 1; 003, n = 1 and 247 others
- Name: 249 entries: Aahaan, n = 1; Abdihamid, n = 1; Abdulkarim, n = 1 and 246 others (1 missing)
- Sex: 2 entries: Female, n = 139; Male, n = 110 (1 missing)
- Age: Mean = 50.09, SD = 14.54, range = [25, 73], 1 missing
- Race: 6 entries: White, n = 165; Hispanic, n = 37; Black, n = 33 and 3 others (1 missing)
- PreinvasiveComponent: 2 entries: Absent, n = 193; Present, n = 56 (1 missing)
- LVI: 2 entries: Absent, n = 161; Present, n = 89
- PNI: 2 entries: Absent, n = 169; Present, n = 80 (1 missing)
- Death: 2 levels: FALSE (n = 64); TRUE (n = 185) and missing (n = 1)
- Group: 2 entries: Treatment, n = 137; Control, n = 112 (1 missing)
- Grade: 3 entries: 3, n = 112; 2, n = 71; 1, n = 66 (1 missing)
- TStage: 4 entries: 4, n = 108; 3, n = 85; 2, n = 37 and 1 other
- AntiX_intensity: Mean = 2.38, SD = 0.66, range = [1, 3], 1 missing
- AntiY_intensity: Mean = 2.02, SD = 0.76, range = [1, 3], 1 missing
- LymphNodeMetastasis: 2 entries: Absent, n = 158; Present, n = 91 (1 missing)
- Valid: 2 levels: FALSE (n = 119); TRUE (n = 130) and missing (n = 1)
- Smoker: 2 levels: FALSE (n = 115); TRUE (n = 134) and missing (n = 1)
- Grade_Level: 3 entries: high, n = 110; moderate, n = 77; low, n = 62 (1 missing)
- DeathTime: 2 entries: Within1Year, n = 148; MoreThan1Year, n = 102
Table 1 via arsenal 📦
# cat(names(mydata), sep = " + \n")
library(arsenal)
tab1 <- arsenal::tableby(
~ Sex +
Age +
Race +
PreinvasiveComponent +
LVI +
PNI +
Death +
Group +
Grade +
TStage +
# `Anti-X-intensity` +
# `Anti-Y-intensity` +
LymphNodeMetastasis +
Valid +
Smoker +
Grade_Level
,
data = mydata
)
summary(tab1)| Overall (N=250) | |
|---|---|
| Sex | |
| N-Miss | 1 |
| Female | 139 (55.8%) |
| Male | 110 (44.2%) |
| Age | |
| N-Miss | 1 |
| Mean (SD) | 50.092 (14.544) |
| Range | 25.000 - 73.000 |
| Race | |
| N-Miss | 1 |
| Asian | 6 (2.4%) |
| Bi-Racial | 4 (1.6%) |
| Black | 33 (13.3%) |
| Hispanic | 37 (14.9%) |
| Native | 4 (1.6%) |
| White | 165 (66.3%) |
| PreinvasiveComponent | |
| N-Miss | 1 |
| Absent | 193 (77.5%) |
| Present | 56 (22.5%) |
| LVI | |
| Absent | 161 (64.4%) |
| Present | 89 (35.6%) |
| PNI | |
| N-Miss | 1 |
| Absent | 169 (67.9%) |
| Present | 80 (32.1%) |
| Death | |
| N-Miss | 1 |
| FALSE | 64 (25.7%) |
| TRUE | 185 (74.3%) |
| Group | |
| N-Miss | 1 |
| Control | 112 (45.0%) |
| Treatment | 137 (55.0%) |
| Grade | |
| N-Miss | 1 |
| 1 | 66 (26.5%) |
| 2 | 71 (28.5%) |
| 3 | 112 (45.0%) |
| TStage | |
| 1 | 20 (8.0%) |
| 2 | 37 (14.8%) |
| 3 | 85 (34.0%) |
| 4 | 108 (43.2%) |
| LymphNodeMetastasis | |
| N-Miss | 1 |
| Absent | 158 (63.5%) |
| Present | 91 (36.5%) |
| Valid | |
| N-Miss | 1 |
| FALSE | 119 (47.8%) |
| TRUE | 130 (52.2%) |
| Smoker | |
| N-Miss | 1 |
| FALSE | 115 (46.2%) |
| TRUE | 134 (53.8%) |
| Grade_Level | |
| N-Miss | 1 |
| high | 110 (44.2%) |
| low | 62 (24.9%) |
| moderate | 77 (30.9%) |
Table 1 via tableone 📦
library(tableone)
mydata %>% select(-keycolumns, -dateVariables) %>% tableone::CreateTableOne(data = .)
Overall
n 250
Sex = Male (%) 110 (44.2)
Age (mean (SD)) 50.09 (14.54)
Race (%)
Asian 6 ( 2.4)
Bi-Racial 4 ( 1.6)
Black 33 (13.3)
Hispanic 37 (14.9)
Native 4 ( 1.6)
White 165 (66.3)
PreinvasiveComponent = Present (%) 56 (22.5)
LVI = Present (%) 89 (35.6)
PNI = Present (%) 80 (32.1)
Death = TRUE (%) 185 (74.3)
Group = Treatment (%) 137 (55.0)
Grade (%)
1 66 (26.5)
2 71 (28.5)
3 112 (45.0)
TStage (%)
1 20 ( 8.0)
2 37 (14.8)
3 85 (34.0)
4 108 (43.2)
AntiX_intensity (mean (SD)) 2.38 (0.66)
AntiY_intensity (mean (SD)) 2.02 (0.76)
LymphNodeMetastasis = Present (%) 91 (36.5)
Valid = TRUE (%) 130 (52.2)
Smoker = TRUE (%) 134 (53.8)
Grade_Level (%)
high 110 (44.2)
low 62 (24.9)
moderate 77 (30.9)
DeathTime = Within1Year (%) 148 (59.2)
Descriptive Statistics of Continuous Variables
mydata %>% select(continiousVariables, numericVariables, integerVariables) %>% summarytools::descr(.,
style = "rmarkdown")# A tibble: 15 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 Sex chr 1 0.4 3 NA NA NA
2 PreinvasiveComponent chr 1 0.4 3 NA NA NA
3 LVI chr 0 0 2 NA NA NA
4 PNI chr 1 0.4 3 NA NA NA
5 Death lgl 1 0.4 3 0 0.74 1
6 Group chr 1 0.4 3 NA NA NA
7 Grade chr 1 0.4 4 NA NA NA
8 TStage chr 0 0 4 NA NA NA
9 AntiX_intensity dbl 1 0.4 4 1 2.38 3
10 AntiY_intensity dbl 1 0.4 4 1 2.02 3
11 LymphNodeMetastasis chr 1 0.4 3 NA NA NA
12 Valid lgl 1 0.4 3 0 0.52 1
13 Smoker lgl 1 0.4 3 0 0.54 1
14 Grade_Level chr 1 0.4 4 NA NA NA
15 DeathTime chr 0 0 2 NA NA NA
# A tibble: 17 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 Name chr 1 0.4 250 NA NA NA
2 Sex chr 1 0.4 3 NA NA NA
3 Age dbl 1 0.4 50 25 50.1 73
4 Race chr 1 0.4 7 NA NA NA
5 PreinvasiveComponent chr 1 0.4 3 NA NA NA
6 PNI chr 1 0.4 3 NA NA NA
7 LastFollowUpDate dat 1 0.4 13 NA NA NA
8 Death lgl 1 0.4 3 0 0.74 1
9 Group chr 1 0.4 3 NA NA NA
10 Grade chr 1 0.4 4 NA NA NA
11 AntiX_intensity dbl 1 0.4 4 1 2.38 3
12 AntiY_intensity dbl 1 0.4 4 1 2.02 3
13 LymphNodeMetastasis chr 1 0.4 3 NA NA NA
14 Valid lgl 1 0.4 3 0 0.52 1
15 Smoker lgl 1 0.4 3 0 0.54 1
16 Grade_Level chr 1 0.4 4 NA NA NA
17 SurgeryDate dat 1 0.4 219 NA NA NA
# A tibble: 21 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 ID chr 0 0 250 NA NA NA
2 Name chr 1 0.4 250 NA NA NA
3 Sex chr 1 0.4 3 NA NA NA
4 Age dbl 1 0.4 50 25 50.1 73
5 Race chr 1 0.4 7 NA NA NA
6 PreinvasiveComponent chr 1 0.4 3 NA NA NA
7 LVI chr 0 0 2 NA NA NA
8 PNI chr 1 0.4 3 NA NA NA
9 LastFollowUpDate dat 1 0.4 13 NA NA NA
10 Death lgl 1 0.4 3 0 0.74 1
# … with 11 more rows
Use R/gc_desc_cat.R to generate gc_desc_cat.Rmd containing descriptive statistics for categorical variables
mydata %>% janitor::tabyl(Sex) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Sex | n | percent | valid_percent |
|---|---|---|---|
| Female | 139 | 55.6% | 55.8% |
| Male | 110 | 44.0% | 44.2% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Race) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Race | n | percent | valid_percent |
|---|---|---|---|
| Asian | 6 | 2.4% | 2.4% |
| Bi-Racial | 4 | 1.6% | 1.6% |
| Black | 33 | 13.2% | 13.3% |
| Hispanic | 37 | 14.8% | 14.9% |
| Native | 4 | 1.6% | 1.6% |
| White | 165 | 66.0% | 66.3% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(PreinvasiveComponent) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| PreinvasiveComponent | n | percent | valid_percent |
|---|---|---|---|
| Absent | 193 | 77.2% | 77.5% |
| Present | 56 | 22.4% | 22.5% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(LVI) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| LVI | n | percent |
|---|---|---|
| Absent | 161 | 64.4% |
| Present | 89 | 35.6% |
mydata %>% janitor::tabyl(PNI) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| PNI | n | percent | valid_percent |
|---|---|---|---|
| Absent | 169 | 67.6% | 67.9% |
| Present | 80 | 32.0% | 32.1% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Group) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Group | n | percent | valid_percent |
|---|---|---|---|
| Control | 112 | 44.8% | 45.0% |
| Treatment | 137 | 54.8% | 55.0% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Grade) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Grade | n | percent | valid_percent |
|---|---|---|---|
| 1 | 66 | 26.4% | 26.5% |
| 2 | 71 | 28.4% | 28.5% |
| 3 | 112 | 44.8% | 45.0% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(TStage) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| TStage | n | percent |
|---|---|---|
| 1 | 20 | 8.0% |
| 2 | 37 | 14.8% |
| 3 | 85 | 34.0% |
| 4 | 108 | 43.2% |
mydata %>% janitor::tabyl(LymphNodeMetastasis) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| LymphNodeMetastasis | n | percent | valid_percent |
|---|---|---|---|
| Absent | 158 | 63.2% | 63.5% |
| Present | 91 | 36.4% | 36.5% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Grade_Level) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Grade_Level | n | percent | valid_percent |
|---|---|---|---|
| high | 110 | 44.0% | 44.2% |
| low | 62 | 24.8% | 24.9% |
| moderate | 77 | 30.8% | 30.9% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(DeathTime) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| DeathTime | n | percent |
|---|---|---|
| MoreThan1Year | 102 | 40.8% |
| Within1Year | 148 | 59.2% |
race_stats <- summarytools::freq(mydata$Race)
print(race_stats, report.nas = FALSE, totals = FALSE, display.type = FALSE, Variable.label = "Race Group")variable = PreinvasiveComponent
type = character
na = 1 of 250 (0.4%)
unique = 3
Absent = 193 (77.2%)
Present = 56 (22.4%)
NA = 1 (0.4%)
## Frequency or custom tables for categorical variables
SmartEDA::ExpCTable(mydata, Target = NULL, margin = 1, clim = 10, nlim = 5, round = 2,
bin = NULL, per = T) Variable Valid Frequency Percent CumPercent
1 Sex Female 139 55.6 55.6
2 Sex Male 110 44.0 99.6
3 Sex NA 1 0.4 100.0
4 Sex TOTAL 250 NA NA
5 Race Asian 6 2.4 2.4
6 Race Bi-Racial 4 1.6 4.0
7 Race Black 33 13.2 17.2
8 Race Hispanic 37 14.8 32.0
9 Race NA 1 0.4 32.4
10 Race Native 4 1.6 34.0
11 Race White 165 66.0 100.0
12 Race TOTAL 250 NA NA
13 PreinvasiveComponent Absent 193 77.2 77.2
14 PreinvasiveComponent NA 1 0.4 77.6
15 PreinvasiveComponent Present 56 22.4 100.0
16 PreinvasiveComponent TOTAL 250 NA NA
17 LVI Absent 161 64.4 64.4
18 LVI Present 89 35.6 100.0
19 LVI TOTAL 250 NA NA
20 PNI Absent 169 67.6 67.6
21 PNI NA 1 0.4 68.0
22 PNI Present 80 32.0 100.0
23 PNI TOTAL 250 NA NA
24 Group Control 112 44.8 44.8
25 Group NA 1 0.4 45.2
26 Group Treatment 137 54.8 100.0
27 Group TOTAL 250 NA NA
28 Grade 1 66 26.4 26.4
29 Grade 2 71 28.4 54.8
30 Grade 3 112 44.8 99.6
31 Grade NA 1 0.4 100.0
32 Grade TOTAL 250 NA NA
33 TStage 1 20 8.0 8.0
34 TStage 2 37 14.8 22.8
35 TStage 3 85 34.0 56.8
36 TStage 4 108 43.2 100.0
37 TStage TOTAL 250 NA NA
38 LymphNodeMetastasis Absent 158 63.2 63.2
39 LymphNodeMetastasis NA 1 0.4 63.6
40 LymphNodeMetastasis Present 91 36.4 100.0
41 LymphNodeMetastasis TOTAL 250 NA NA
42 Grade_Level high 110 44.0 44.0
43 Grade_Level low 62 24.8 68.8
44 Grade_Level moderate 77 30.8 99.6
45 Grade_Level NA 1 0.4 100.0
46 Grade_Level TOTAL 250 NA NA
47 DeathTime MoreThan1Year 102 40.8 40.8
48 DeathTime Within1Year 148 59.2 100.0
49 DeathTime TOTAL 250 NA NA
50 AntiX_intensity 1 25 10.0 10.0
51 AntiX_intensity 2 104 41.6 51.6
52 AntiX_intensity 3 120 48.0 99.6
53 AntiX_intensity NA 1 0.4 100.0
54 AntiX_intensity TOTAL 250 NA NA
55 AntiY_intensity 1 69 27.6 27.6
56 AntiY_intensity 2 105 42.0 69.6
57 AntiY_intensity 3 75 30.0 99.6
58 AntiY_intensity NA 1 0.4 100.0
59 AntiY_intensity TOTAL 250 NA NA
# A tibble: 16 x 5
col_name cnt common common_pcnt levels
<chr> <int> <chr> <dbl> <named list>
1 Death 3 TRUE 74 <tibble [3 × 3]>
2 DeathTime 2 Within1Year 59.2 <tibble [2 × 3]>
3 Grade 4 3 44.8 <tibble [4 × 3]>
4 Grade_Level 4 high 44 <tibble [4 × 3]>
5 Group 3 Treatment 54.8 <tibble [3 × 3]>
6 ID 250 001 0.4 <tibble [250 × 3]>
7 LVI 2 Absent 64.4 <tibble [2 × 3]>
8 LymphNodeMetastasis 3 Absent 63.2 <tibble [3 × 3]>
9 Name 250 Aahaan 0.4 <tibble [250 × 3]>
10 PNI 3 Absent 67.6 <tibble [3 × 3]>
11 PreinvasiveComponent 3 Absent 77.2 <tibble [3 × 3]>
12 Race 7 White 66 <tibble [7 × 3]>
13 Sex 3 Female 55.6 <tibble [3 × 3]>
14 Smoker 3 TRUE 53.6 <tibble [3 × 3]>
15 TStage 4 4 43.2 <tibble [4 × 3]>
16 Valid 3 TRUE 52 <tibble [3 × 3]>
# A tibble: 3 x 3
value prop cnt
<chr> <dbl> <int>
1 Treatment 0.548 137
2 Control 0.448 112
3 <NA> 0.004 1
summarytools::stby(list(x = mydata$LVI, y = mydata$LymphNodeMetastasis), mydata$PNI,
summarytools::ctable)mydata %>% select(characterVariables) %>% select(PreinvasiveComponent, PNI, LVI) %>%
reactable::reactable(data = ., groupBy = c("PreinvasiveComponent", "PNI"), columns = list(LVI = reactable::colDef(aggregate = "count")))Descriptive Statistics Age
mydata %>% jmv::descriptives(data = ., vars = "Age", hist = TRUE, dens = TRUE, box = TRUE,
violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE, skew = TRUE,
kurt = TRUE, quart = TRUE)
DESCRIPTIVES
Descriptives
──────────────────────────────────
Age
──────────────────────────────────
N 249
Missing 1
Mean 50.1
Median 51.0
Mode 70.0
Standard deviation 14.5
Variance 212
Minimum 25.0
Maximum 73.0
Skewness -0.0856
Std. error skewness 0.154
Kurtosis -1.24
Std. error kurtosis 0.307
25th percentile 38.0
50th percentile 51.0
75th percentile 63.0
──────────────────────────────────
Descriptive Statistics AntiX_intensity
mydata %>% jmv::descriptives(data = ., vars = "AntiX_intensity", hist = TRUE, dens = TRUE,
box = TRUE, violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE,
skew = TRUE, kurt = TRUE, quart = TRUE)
DESCRIPTIVES
Descriptives
──────────────────────────────────────────
AntiX_intensity
──────────────────────────────────────────
N 249
Missing 1
Mean 2.38
Median 2.00
Mode 3.00
Standard deviation 0.662
Variance 0.439
Minimum 1.00
Maximum 3.00
Skewness -0.606
Std. error skewness 0.154
Kurtosis -0.656
Std. error kurtosis 0.307
25th percentile 2.00
50th percentile 2.00
75th percentile 3.00
──────────────────────────────────────────
Descriptive Statistics AntiY_intensity
mydata %>% jmv::descriptives(data = ., vars = "AntiY_intensity", hist = TRUE, dens = TRUE,
box = TRUE, violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE,
skew = TRUE, kurt = TRUE, quart = TRUE)
DESCRIPTIVES
Descriptives
──────────────────────────────────────────
AntiY_intensity
──────────────────────────────────────────
N 249
Missing 1
Mean 2.02
Median 2.00
Mode 2.00
Standard deviation 0.762
Variance 0.580
Minimum 1.00
Maximum 3.00
Skewness -0.0405
Std. error skewness 0.154
Kurtosis -1.27
Std. error kurtosis 0.307
25th percentile 1.00
50th percentile 2.00
75th percentile 3.00
──────────────────────────────────────────
Overall
n 250
Age (mean (SD)) 50.09 (14.54)
AntiX_intensity (mean (SD)) 2.38 (0.66)
AntiY_intensity (mean (SD)) 2.02 (0.76)
Overall
n 250
Age (mean (SD)) 50.09 (14.54)
AntiX_intensity (mean (SD)) 2.38 (0.66)
AntiY_intensity (mean (SD)) 2.02 (0.76)
variable = Age
type = double
na = 1 of 250 (0.4%)
unique = 50
min|max = 25 | 73
q05|q95 = 27 | 71.6
q25|q75 = 38 | 63
median = 51
mean = 50.09237
mydata %>% select(continiousVariables) %>% SmartEDA::ExpNumStat(data = ., by = "A",
gp = NULL, Qnt = seq(0, 1, 0.1), MesofShape = 2, Outlier = TRUE, round = 2)# A tibble: 3 x 10
col_name min q1 median mean q3 max sd pcnt_na hist
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <named list>
1 Age 25 38 51 50.1 63 73 14.5 0.4 <tibble [12…
2 AntiX_intens… 1 2 2 2.38 3 3 0.662 0.4 <tibble [12…
3 AntiY_intens… 1 1 2 2.02 3 3 0.762 0.4 <tibble [12…
# A tibble: 27 x 2
value prop
<chr> <dbl>
1 [-Inf, 24) 0
2 [24, 26) 0.0201
3 [26, 28) 0.0442
4 [28, 30) 0.0281
5 [30, 32) 0.0522
6 [32, 34) 0.0321
7 [34, 36) 0.0402
8 [36, 38) 0.0321
9 [38, 40) 0.0361
10 [40, 42) 0.0402
# … with 17 more rows
summarytools::stby(data = mydata, INDICES = mydata$PreinvasiveComponent, FUN = summarytools::descr,
stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE)with(mydata, summarytools::stby(Age, PreinvasiveComponent, summarytools::descr),
stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE)## Summary statistics by – category
SmartEDA::ExpNumStat(mydata, by = "GA", gp = "PreinvasiveComponent", Qnt = seq(0,
1, 0.1), MesofShape = 2, Outlier = TRUE, round = 2) Vname Group TN nNeg nZero nPos NegInf PosInf NA_Value
1 Age PreinvasiveComponent:All 250 0 0 249 0 0 1
2 Age PreinvasiveComponent:Absent 193 0 0 193 0 0 0
3 Age PreinvasiveComponent:Present 56 0 0 55 0 0 1
4 Age PreinvasiveComponent:NA 0 0 0 0 0 0 0
Per_of_Missing sum min max mean median SD CV IQR Skewness Kurtosis
1 0.40 12473 25 73 50.09 51 14.54 0.29 25.0 -0.09 -1.24
2 0.00 9572 25 73 49.60 51 14.53 0.29 26.0 -0.08 -1.27
3 1.79 2865 26 73 52.09 52 14.57 0.28 22.5 -0.14 -1.14
4 NaN 0 Inf -Inf NaN NA NA NA NA NaN NaN
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% LB.25% UB.75% nOutliers
1 25 30.0 35.0 41.0 45.0 51 55.0 61.0 65.4 70.0 73 0.50 100.50 0
2 25 29.2 34.4 40.0 44.0 51 55.0 60.4 65.0 69.0 73 -2.00 102.00 0
3 26 31.4 36.6 43.4 48.6 52 56.4 61.8 69.2 71.6 73 6.75 96.75 0
4 NA NA NA NA NA NA NA NA NA NA NA NA NA 0
Codes for Survival Analysis24
https://link.springer.com/article/10.1007/s00701-019-04096-9
Calculate survival time
mydata$int <- lubridate::interval(lubridate::ymd(mydata$SurgeryDate), lubridate::ymd(mydata$LastFollowUpDate))
mydata$OverallTime <- lubridate::time_length(mydata$int, "month")
mydata$OverallTime <- round(mydata$OverallTime, digits = 1)recode death status outcome as numbers for survival analysis
## Recoding mydata$Death into mydata$Outcome
mydata$Outcome <- forcats::fct_recode(as.character(mydata$Death), `1` = "TRUE", `0` = "FALSE")
mydata$Outcome <- as.numeric(as.character(mydata$Outcome))it is always a good practice to double-check after recoding25
0 1
FALSE 64 0
TRUE 0 185
library(survival)
# data(lung) km <- with(lung, Surv(time, status))
km <- with(mydata, Surv(OverallTime, Outcome))
head(km, 80) [1] 3.7 11.0 7.0 3.2 10.6 10.4 8.9 NA+ 10.2 9.5 8.2 5.1
[13] 7.0 3.0 5.0 9.4 7.3 9.8 4.2+ 10.9 11.3+ 4.7+ 11.8+ 3.1+
[25] 7.6+ 8.2 8.8 3.0 NA+ 6.2+ 10.8 7.8 9.4 6.2+ 10.7 7.1
[37] 11.3+ 4.0+ 3.8 9.2 5.1 11.2 5.4 6.2 5.3 6.6 6.6 6.1
[49] 3.9 5.2 9.3+ 7.5 9.5+ 6.4+ 10.6 3.8+ 4.7 10.6 5.2 11.1
[61] 3.0 5.6+ 4.7+ 6.4 7.2 7.0 5.0+ 2.9+ 5.0 4.9+ 3.9 4.7
[73] 10.8 10.8 8.4 11.4 4.3+ 8.2+ 4.7+ 4.5+
Kaplan-Meier Plot Log-Rank Test
# Drawing Survival Curves Using ggplot2
# https://rpkgs.datanovia.com/survminer/reference/ggsurvplot.html
dependentKM <- "Surv(OverallTime, Outcome)"
explanatoryKM <- "LVI"
mydata %>%
finalfit::surv_plot(.data = .,
dependent = dependentKM,
explanatory = explanatoryKM,
xlab='Time (months)',
pval=TRUE,
legend = 'none',
break.time.by = 12,
xlim = c(0,60)
# legend.labs = c('a','b')
)# Drawing Survival Curves Using ggplot2
# https://rpkgs.datanovia.com/survminer/reference/ggsurvplot.html
mydata %>%
finalfit::surv_plot(.data = .,
dependent = "Surv(OverallTime, Outcome)",
explanatory = "LVI",
xlab='Time (months)',
pval=TRUE,
legend = 'none',
break.time.by = 12,
xlim = c(0,60)
# legend.labs = c('a','b')
)library(finalfit)
library(survival)
explanatoryUni <- "LVI"
dependentUni <- "Surv(OverallTime, Outcome)"
tUni <- mydata %>% finalfit::finalfit(dependentUni, explanatoryUni)
knitr::kable(tUni, row.names = FALSE, align = c("l", "l", "r", "r", "r", "r"))| Dependent: Surv(OverallTime, Outcome) | all | HR (univariable) | HR (multivariable) | |
|---|---|---|---|---|
| LVI | Absent | 161 (100.0) | - | - |
| Present | 89 (100.0) | 2.04 (1.49-2.80, p<0.001) | 2.04 (1.49-2.80, p<0.001) |
tUni_df <- tibble::as_tibble(tUni, .name_repair = "minimal") %>% janitor::clean_names()
tUni_df_descr <- paste0("When ", tUni_df$dependent_surv_overall_time_outcome[1],
" is ", tUni_df$x[2], ", there is ", tUni_df$hr_univariable[2], " times risk than ",
"when ", tUni_df$dependent_surv_overall_time_outcome[1], " is ", tUni_df$x[1],
".")When LVI is Present, there is 2.04 (1.49-2.80, p<0.001) times risk than when LVI is Absent.
Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
3 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
LVI=Absent 158 121 20.7 13.5 26.4
LVI=Present 89 64 10.0 8.9 11.2
km_fit_median_df <- summary(km_fit)
km_fit_median_df <- as.data.frame(km_fit_median_df$table) %>% janitor::clean_names() %>%
tibble::rownames_to_column()km_fit_median_definition <- km_fit_median_df %>% dplyr::mutate(description = glue::glue("When {rowname}, median survival is {median} [{x0_95lcl} - {x0_95ucl}, 95% CI] months.")) %>%
dplyr::select(description) %>% pull()When LVI=Absent, median survival is 20.7 [13.5 - 26.4, 95% CI] months., When LVI=Present, median survival is 10 [8.9 - 11.2, 95% CI] months.
Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
3 observations deleted due to missingness
LVI=Absent
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 81 58 0.600 0.0409 0.524 0.685
36 22 47 0.211 0.0372 0.150 0.299
LVI=Present
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 17 50 0.3112 0.0576 0.2165 0.447
36 3 12 0.0741 0.0379 0.0272 0.202
km_fit_summary <- summary(km_fit, times = c(12, 36, 60))
km_fit_df <- as.data.frame(km_fit_summary[c("strata", "time", "n.risk", "n.event",
"surv", "std.err", "lower", "upper")])km_fit_definition <- km_fit_df %>% dplyr::mutate(description = glue::glue("When {strata}, {time} month survival is {scales::percent(surv)} [{scales::percent(lower)}-{scales::percent(upper)}, 95% CI].")) %>%
dplyr::select(description) %>% pull()When LVI=Absent, 12 month survival is 60.0% [52.4%-68.5%, 95% CI]., When LVI=Absent, 36 month survival is 21.1% [15.0%-29.9%, 95% CI]., When LVI=Present, 12 month survival is 31.1% [21.7%-44.7%, 95% CI]., When LVI=Present, 36 month survival is 7.4% [2.7%-20.2%, 95% CI].
dependentKM <- "Surv(OverallTime, Outcome)"
explanatoryKM <- "TStage"
mydata %>%
finalfit::surv_plot(.data = .,
dependent = dependentKM,
explanatory = explanatoryKM,
xlab='Time (months)',
pval=TRUE,
legend = 'none',
break.time.by = 12,
xlim = c(0,60)
# legend.labs = c('a','b')
)Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
3 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
LVI=Absent 158 121 20.7 13.5 26.4
LVI=Present 89 64 10.0 8.9 11.2
print(km_fit,
scale=1,
digits = max(options()$digits - 4,3),
print.rmean=getOption("survfit.print.rmean"),
rmean = getOption('survfit.rmean'),
print.median=getOption("survfit.print.median"),
median = getOption('survfit.median')
)Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
3 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
LVI=Absent 158 121 20.7 13.5 26.4
LVI=Present 89 64 10.0 8.9 11.2
Interpret the results in context of the working hypothesis elaborated in the introduction and other relevant studies; include a discussion of limitations of the study.
Discuss potential clinical applications and implications for future research
Knijn, N., F. Simmer, and I. D. Nagtegaal. 2015. “Recommendations for Reporting Histopathology Studies: A Proposal.” Virchows Archiv 466 (6): 611–15. https://doi.org/10.1007/s00428-015-1762-3.
Schmidt, Robert L., Deborah J. Chute, Jorie M. Colbert-Getz, Adolfo Firpo-Betancourt, Daniel S. James, Julie K. Karp, Douglas C. Miller, et al. 2017. “Statistical Literacy Among Academic Pathologists: A Survey Study to Gauge Knowledge of Frequently Used Statistical Tests Among Trainees and Faculty.” Archives of Pathology & Laboratory Medicine 141 (2): 279–87. https://doi.org/10.5858/arpa.2016-0200-OA.
From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460276/↩︎
From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460276/↩︎
See childRmd/_01header.Rmd file for other general settings↩︎
Change echo = FALSE to hide codes after knitting.↩︎
See childRmd/_02fakeData.Rmd file for other codes↩︎
Synthea The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures. BMC Med Inform Decis Mak 19, 44 (2019) doi:10.1186/s12911-019-0793-0↩︎
https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-0793-0↩︎
https://medium.com/free-code-camp/how-our-test-data-generator-makes-fake-data-look-real-ace01c5bde4a↩︎
lung, cancer, breast datası ile birleştir↩︎
See childRmd/_03importData.Rmd file for other codes↩︎
See childRmd/_04briefSummary.Rmd file for other codes↩︎
Kişisel verilerin kaydedilmesi ve kişisel verileri hukuka aykırı olarak verme veya ele geçirme Türk Ceza Kanunu’nun 135. ve 136. maddesi kapsamında bizim hukuk sistemimizde suç olarak tanımlanmıştır. Kişisel verilerin kaydedilmesi suçunun cezası 1 ila 3 yıl hapis cezasıdır. Suçun nitelikli hali ise, kamu görevlisi tarafından görevin verdiği yetkinin kötüye kullanılarak veya belirli bir meslek veya sanatın sağladığı kolaylıktan yararlanılarak işlenmesidir ki bu durumda suçun cezası 1.5 ile 4.5 yıl hapis cezası olacaktır.↩︎
See childRmd/_06variableTypes.Rmd file for other codes↩︎
See childRmd/_07overView.Rmd file for other codes↩︎
Statistical Literacy Among Academic Pathologists: A Survey Study to Gauge Knowledge of Frequently Used Statistical Tests Among Trainees and Faculty. Archives of Pathology & Laboratory Medicine: February 2017, Vol. 141, No. 2, pp. 279-287. https://doi.org/10.5858/arpa.2016-0200-OA↩︎
From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460276/↩︎
See childRmd/_11descriptives.Rmd file for other codes↩︎
See childRmd/_18survival.Rmd file for other codes, and childRmd/_19shinySurvival.Rmd for shiny application↩︎
JAMA retraction after miscoding – new Finalfit function to check recoding↩︎
See childRmd/_23footer.Rmd file for other codes↩︎
Smith AM, Katz DS, Niemeyer KE, FORCE11 Software Citation Working Group. (2016) Software Citation Principles. PeerJ Computer Science 2:e86. DOI: 10.7717/peerj-cs.86 https://www.force11.org/software-citation-principles↩︎
A work by Serdar Balci
drserdarbalci@gmail.com